Mobile advertising is widely used by advertisers to market their products via mobile devices. Given the widespread availability of mobile devices, mobile advertising can be an extremely effective way for advertisers to reach a wide mass of potential customers and induce numerous users to purchase their products. By targeting mobile users with effective mobile advertisements, advertisers can yield large financial returns from their mobile advertisements. Not surprisingly, many advertisers continuously measure the performance of their advertisements to understand how, if necessary, they can optimize their advertisements for a better performance.
One difficulty in evaluating the performance of mobile advertising is the collection of data regarding user engagement. In general, the reporting of data regarding user engagement currently requires a significant amount of human resources. For example, the architecture for many existing mobile advertising units requires that the units be hard-coded by the developer of the mobile advertising unit to identify events of interest for the mobile advertising unit and to generate messages for the developer to evaluate the mobile advertising unit. However, the identification or tagging of specific events is typically not standardized. That is, different developers may be interested in different aspect of user interaction.
One result of the foregoing process is that analysis of the performance of a particular mobile advertisement unit becomes inaccurate. In particular, since events in such units are not standardized, this typically requires that a human operator review the results available for a unit and infer conclusions solely from this available data. This introduces a subjective element into the process and thus the analysis may result in conclusions that are inaccurate, incomplete, or completely wrong. Another result of this process is that since different mobile advertising units will generate differing sets of data regarding user engagement, it is difficult to make head-to-head comparisons between different mobile advertisements. This problem is also exacerbated by human intervention. In particular, the selection of events to compare or analyze for comparison is subjective. Accordingly, the process again may again result in conclusions that are inaccurate, incomplete, or completely wrong.